Multilinear regression is a statistical method used to analyze the relationship between multiple independent variables and a single dependent variable. It is commonly used to predict the values of the dependent variable based on the values of the independent variables.
The purpose of multilinear regression is to understand the influence of each independent variable on the dependent variable and to create a predictive model that accurately represents the relationship between these variables.
The results of a multilinear regression analysis include a regression equation, which shows the relationship between the dependent variable and the independent variables, as well as statistical measures such as R-squared and p-values that indicate the strength and significance of the relationship.
Simple linear regression involves only one independent variable, while multilinear regression involves two or more independent variables. This allows for a more complex understanding of the relationship between variables and can provide more accurate predictions.
Some common errors that may occur include multicollinearity, which is when independent variables are highly correlated, and heteroscedasticity, which is when the variance of the dependent variable is not constant across all levels of the independent variables. These errors can affect the accuracy of the regression model and should be addressed before interpreting the results.